Mechanism Design & Auctions

Introduces second-price sealed-bid (Vickrey) auction; establishes truthful bidding as dominant strategy.

Characterizes revenue-maximizing auctions; introduces virtual valuations and optimal reserves; Nobel Prize work.

Independently proves revenue equivalence theorem; shows optimal reserve prices increase revenue.

Introduces affiliated values and linkage principle; shows English auctions reduce winner's curse.

Demonstrates one additional bidder in simple auction outperforms optimally-designed negotiation; establishes competition's fundamental value in asymmetric settings.

Extends affiliated values to common value settings with asymmetric information; essential for understanding winner's curse in display advertising.

Practitioner's guide to designing real auctions; details SMRA for spectrum auctions; Nobel Prize foundation.

Explains why VCG rarely used in practice; introduces practical alternatives for combinatorial settings.

Comprehensive treatment of package bidding, winner determination, and spectrum auction design.

Generalizes incentive-compatible mechanisms with transfers; completes theoretical foundation for VCG.

Proposes 'clinching' auction achieving Vickrey outcomes dynamically while preserving privacy; eliminates demand reduction in uniform-price settings. Adopted in Treasury and spectrum auctions.

Introduces mechanisms generating competitive revenues while minimizing shill-bidding incentives; theoretical foundation for FCC combinatorial clock auction payment rules.

Develops deferred-acceptance clock auctions with strategy-proof properties; directly applied to FCC Incentive Auction ($19.8B, 2016-17). Nobel Prize-awarded work.

Introduces adaptive pacing strategies for budget-constrained bidders; proves regret bounds; foundational for ad auction budget management.

Analyzes fluid approximations for budget-constrained repeated auctions; practical design principles.

Compares pacing, throttling, and probabilistic strategies; Google research on platform budget management.

Analyzes VCG with value-maximizing autobidders; shows cost multipliers improve welfare.

Proves first-price auctions with budget pacing guarantee equilibrium uniqueness and efficient computation; theoretical foundation for industry's second-to-first price transition.

Foundational paper proving existence of pacing equilibria for budget-constrained bidders; connects repeated auctions to competitive equilibrium theory.

Compares value-maximizing versus utility-maximizing autobidders; shows first-best revenue achievable with value-maximizers. Essential for understanding modern autobidding platforms.

Extends autobidding theory to budget-constrained value maximizers; proposes clipping mechanisms achieving near-optimal revenue.

Practical algorithms for learning optimal reserve prices at Google scale; bridges theory and deployed systems.

Introduces deferred acceptance algorithm; proves existence of stable matchings; Nobel Prize foundation.

Applies matching theory to school choice; shows Boston mechanism is manipulable; proposes strategy-proof alternatives.

Designs kidney exchange clearinghouses using top trading cycles; Nobel Prize application.

Documents Boston's switch to deferred acceptance; empirical evidence on strategic behavior.

Reports deployment of A-CEEI mechanism at Wharton (1,700 students, 350 courses); solves billions of mixed-integer programs. Definitive paper on scaling market design.

Studies optimal adoption policies on matching platforms using Food Rescue U.S. data; shows commitment mechanisms' impacts on growth versus engagement.

Proposes proxy bidding countering strategic gaming in course allocation; key insight for automated assignment systems.

Foundational cheap talk model; shows partition equilibria arise with interest misalignment.

Introduces information design framework; characterizes sender-optimal signals via concavification; transformed the field.

Extends Bayesian persuasion to multiple senders; shows competition increases information revelation.

Comprehensive survey connecting Bayesian persuasion, mechanism design, and correlated equilibrium.

Characterizes equilibrium bidding across all possible information structures in first-price auctions; derives robust revenue bounds regardless of bidder information.

Characterizes revenue-maximizing information: reveal low values (increase competition) but pool high values. Provides rationale for conflation strategies in digital advertising.

First rigorous analysis of GSP mechanism used by Google/Yahoo; proves GSP lacks dominant strategy but identifies 'locally envy-free' equilibrium.

Independent equilibrium analysis of search auctions; introduces quality-weighted ranking (bid × CTR); validates theory with Google data.

Authoritative survey integrating mechanism design, optimization, and ML perspectives on ad auctions.

First large-scale field experiment applying Myerson optimal auction theory to sponsored search at Yahoo; demonstrates ~12% revenue increase from optimized reserves.

Formulates RTB as functional optimization; derives optimal bidding functions under budget constraints. Foundational for DSP bidding strategies.

Addresses industry's second-to-first price transition; proposes gradient-based reserve optimization validated on Google Ad Exchange data.

Foundational paper coining the field; establishes computational complexity constraints on mechanism design through scheduling problems where VCG fails.

Proves simple mechanisms (Vickrey with anonymous reserves) achieve constant-factor approximation to optimal revenue; establishes simplicity-optimality tradeoff paradigm.

Introduces sequential posted pricing as approximation to optimal multi-dimensional revenue; connects prophet inequalities to mechanism design.

Fundamental reduction showing every mechanism implementable as distribution over virtual VCG rules. FOCS Test of Time Award.

Generalizes prophet inequality to matroid constraints; first efficient constant-approximations for multi-parameter settings.

General theory of monopoly platform pricing showing how platforms internalize network externalities only at the margin; foundational for understanding platform fee structures.

Models platform pricing when consumers value variety; shows variable fees trade off producer innovation against platform holdup.

Comprehensive theory of firm choice between vertical integration and platform modes; directly applicable to Uber, Airbnb marketplace design.

Explores platform creators' choices of price structure, price level, and investment in online labor markets; early theoretical treatment of gig economy mechanism design.

Demonstrates transparency reduces worker bargaining power in platform/gig markets using event-study of state transparency laws; essential for platform wage-setting policy.

Seminal paper showing differential privacy ensures approximate dominant-strategy truthfulness under arbitrary utilities; introduces the exponential mechanism.

Initiates study of data markets through differential privacy lens; shows optimal designs are variants of multi-unit procurement auctions compensating data owners for privacy loss.

Comprehensive survey showing differential privacy provides tools for controlling mechanism stability and designing truthful mechanisms without money.

Reverse-engineers AWS spot pricing revealing hidden dynamic reserve mechanism rather than market-driven prices; critical insights for cloud users and providers.

Combines queuing theory and game theory analyzing when spot instances alongside fixed-price are profitable; accounts for cannibalization and preemption costs.

First online combinatorial auction for cloud handling dynamic arrivals; addresses revenue maximization and truthfulness jointly.

Proposes admission control policies matching heterogeneous workloads to cloud resources via MDPs and information elicitation; significantly improves cluster utilization.